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Kohnert K.-D.,Institute For Diabetes Gerhardt Katsch Karlsburg | Vogt L.,Diabetes Service Center Karlsburg | Salzsieder E.,Institute For Diabetes Gerhardt Katsch Karlsburg
Diabetes Aktuell | Year: 2011

The development of the CGM technology has greatly contributed to achieve the goal of better glycemic control by providing hitherto unknown information on the efficacy of therapeutic adjustments and glucose dynamics. Two variants of CGM based on sensor technology are available: retrospective and real-time glucose monitoring. Both variants differ in their requirements for training and setup time. Computer-based evaluation procedures are available for full utilization of the CGM data by the practicing physician. Clinical study outcomes and data obtained from every-day diabetes management have shown that the use of CGM can consistently improve glycemic control. Longer duration and higher frequency of monitorings may optimize assessment of long-term glycemic control. © Georg Thieme Verlag Stuttgart - New York. Source

Salzsieder E.,Institute of Diabetes Gerhardt Katsch Karlsburg | Vogt L.,Diabetes Service Center Karlsburg | Kohnert K.-D.,Institute of Diabetes Gerhardt Katsch Karlsburg | Heinke P.,Institute of Diabetes Gerhardt Katsch Karlsburg | Augstein P.,Institute of Diabetes Gerhardt Katsch Karlsburg
Computer Methods and Programs in Biomedicine | Year: 2011

The model-based Karlsburg Diabetes Management System (KADIS®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients. © 2010 Elsevier Ireland Ltd. Source

Kohnert K.-D.,Institute of Diabetes Gerhardt Katsch Karlsburg | Freyse E.-J.,Institute of Diabetes Gerhardt Katsch Karlsburg | Salzsieder E.,Institute of Diabetes Gerhardt Katsch Karlsburg | Salzsieder E.,Diabetes Service Center Karlsburg
Current Diabetes Reviews | Year: 2012

The importance of glycaemic variability (GV) as a factor in the pathophysiology of cellular dysfunction and late diabetes complications is currently a matter of debate. However, there is mounting evidence from in vivo and in vitro studies that GV has adverse effects on the cascade of physiological processes that result in chronic ß-cell dysfunctions. Glucose fluctuations more than sustained chronic hyperglycaemia can induce excessive formation of reactive oxygen (ROS) and reactive nitrogen species (RNS), ultimately leading to apoptosis related to oxidative stress. The insulinsecreting ß-cells are particularly susceptible to damage imposed by oxidative stress. Evidence from experiments, using isolated pancreatic islets or ß-cell lines, has linked intermittent high glucose, which mimicks GV under diabetic conditions, to significant impairment of ß-cell function. Several clinical studies reported a close association between GV and ß-cell dysfunction, although the deleterious effects are difficult to demonstrate. Notwithstanding, early therapeutic interventions in patients with type 1 as well as type 2 diabetes, using different strategies of optimising glycaemic control, have shown that favourable outcomes on recovery and maintenance of ß-cell function correlated with reduction of GV. The purpose of the present review is to discuss the detrimental effects of GV and associations with ß-cell function as well as upcoming therapeutic strategies directed towards minimising glucose excursions, improving ß-cell recovery and preventing progressive ß-cell loss. Measuring GV has importance for management of diabetes, because it is the only one component of the dysglycaemia that reflects the degree of dysregulation of glucose homeostasis. © 2012 Bentham Science Publishers. Source

Augstein P.,Institute For Diabetes Gerhardt Katsch Karlsburg | Heinke P.,Institute For Diabetes Gerhardt Katsch Karlsburg | Vogt L.,Diabetes Service Center Karlsburg | Vogt R.,University of Greifswald | And 3 more authors.
BMC Endocrine Disorders | Year: 2015

Background: Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential. Methods: Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles. Results: We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≤12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'. Conclusions: The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies. © 2015 Augstein et al.; licensee BioMed Central. Source

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